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Semantic image representation for image recognition and retrieval using multilayer variational auto-encoder, InceptionNet and low-level image features

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Abstract

This paper presents a novel image descriptor that enhances performance in image recognition and retrieval by combining deep learning and handcrafted features. Our method integrates high-level semantic features extracted via InceptionResNet-V2 with color and texture features to create a comprehensive representation of image content. The descriptor’s effectiveness is demonstrated through extensive experiments across a range of image recognition and retrieval tasks. Our approach is tested on six benchmark datasets, including Corel-1 K, VS, OT, QT, SUN-397, and ILSVRC-2012 for single-label classification, and COCO and NUS-WIDE for multi-label classification, achieving high performances. The results establish that the proposed method is versatile and robust, excelling in single-label and multi-label recognition as well as image retrieval tasks, and outperforms several state-of-the-art methods. This work provides a significant advancement in image representation, with broad applicability in various computer vision domains.

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Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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  1. Department of Computer Engineering, Malayer University, Malayer, Iran

    Davar Giveki & Sajad Esfandyari

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Davar Giveki contributed to conceptualization, methodology, software, supervision, writing, review, and editing. Sajad Esfandyari contributed to software, writing, review, and editing.

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Correspondence to Davar Giveki.

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Giveki, D., Esfandyari, S. Semantic image representation for image recognition and retrieval using multilayer variational auto-encoder, InceptionNet and low-level image features. J Supercomput 81, 346 (2025). https://doi.org/10.1007/s11227-024-06792-5

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